Monocytes are critical cells of the immune system but their role as effectors is relatively poorly understood, as they have long been considered only as precursors of tissue macrophages or dendritic cells. Moreover, it is known that this cell type is heterogeneous, but our understanding of this aspect is limited to the broad classification in classical/intermediate/non-classical monocytes, commonly based on their expression of only two markers, i.e. CD14 and CD16. We deeply dissected the heterogeneity of human circulating monocytes in healthy donors by transcriptomic analysis at single-cell level and identified 9 distinct monocyte populations characterized each by a profile suggestive of specialized functions. The classical monocyte subset in fact included five distinct populations, each enriched for transcriptomic gene sets related to either inflammatory, neutrophil-like, interferon-related, and platelet-related pathways. Non-classical monocytes included two distinct populations, one of which marked specifically by elevated expression levels of complement components. Intermediate monocytes were not further divided in our analysis and were characterized by high levels of human leukocyte antigen (HLA) genes. Finally, we identified one cluster included in both classical and non-classical monocytes, characterized by a strong cytotoxic signature. These findings provided the rationale to exploit the relevance of newly identified monocyte populations in disease evolution. A machine learning approach was developed and applied to two single-cell transcriptome public datasets, from gastrointestinal cancer and Coronavirus disease 2019 (COVID-19) patients. The dissection of these datasets through our classification revealed that patients with advanced cancers showed a selective increase in monocytes enriched in platelet-related pathways. Of note, the signature associated with this population correlated with worse prognosis in gastric cancer patients. Conversely, after immunotherapy, the most activated population was composed of interferon-related monocytes, consistent with an upregulation in interferon-related genes in responder patients compared to non-responders. In COVID-19 patients we confirmed a global activated phenotype of the entire monocyte compartment, but our classification revealed that only cytotoxic monocytes are expanded during the disease progression. Collectively, this study unravels an unexpected complexity among human circulating monocytes and highlights the existence of specialized populations differently engaged depending on the pathological context.

Digital pathology coupled to artificial intelligence (AI)-powered approaches are receiving great attention in the oncoimmunology field, as their adoption holds promise to improve current diagnostic workflows and potentiate the analytic outputs. In this work, we aimed at combining different histopathological approaches and AI-aided analytic tools to analyze the ecosystem of tumor tissues. By deploying AI-powered standard H&E and high-dimensional imaging-mass cytometry (IMC) to FFPE tissue samples, we could extract quantitative and standardized features that couldn’t have been easily identified and integrated by eye. One tissue microarray (TMA) slide containing 108 spots of NSCLC specimens (both adenocarcinoma and squamous carcinoma) was stained with H&E and scanned through the Axio Scan.Z1 (ZEISS) to generate high-quality virtual images. A deep learning algorithm was trained and applied to H&E images to identify tumor cells. The consecutive tissue section was stained with metal-labeled antibodies and processed through the Hyperion workflow (StandardBiotools), allowing quantitative detection of a panel of 23 markers related to tumor cells (Pan-cytokeratin), tissue architecture (aSMA, Vimentin, CD31, Collagen I, nuclei), CD45+ immune cells, comprehensive of myeloid cells (CD68, CD14, CD16, CD163, CD63, CCR4), lymphoid cells (CD3, CD4, CD8, FOXP3, CD20) and immune activation (S100A8, HLA-DR, Granzyme-B, KI67, Arginase-1). Data were exported as MCD files, visualized using the MCD viewer and further analyzed with the Qupath software. Cell segmentation was performed by the CellProfiler and Ilastik softwares and main cell populations were identified by a supervised approach through Cytomap. On H&E images, we generated a classifier of tumor heterogeneity, by exploring the spatial localization of tumor cells with the K-function summary statistic, which analyzes the distribution of tumor cells as a function of their distance. The resulting K-score value was then used to classify each tumor spot as diffuse, poorly clustered or highly clustered. Multiparametric computational analysis of the IMC images allowed to grasp immune and stromal classifiers, including frequency of immune cell populations in the tumor nests versus fibrotic stroma and immune cell interactions. In conclusion, AI-powered analysis of H&E slides is a robust approach that can improve manual scoring and unlock tissue relevant features opening to new diagnostic possibilities. Meanwhile, the analysis of the immune ecosystem by multiparametric imaging mass cytometry allows investigating spatial patterns and cell interactions at single-cell level. Integration of these approaches is feasible and allows the identification of tumor patient profiles with clinical relevance. Citation Format: Alessandra Rigamonti, Marika Viatore, Rebecca Polidori, Marco Erreni, Maria Fumagalli, Daoud Rahal, Massimo Locati, Alberto Mantovani, Federica Marchesi. Integration of AI-powered digital pathology and imaging mass cytometry to identify relevant features of the tumor microenvironment. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5783.